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Analytical modeling and Monte Carlo simulations of multi-parallel slit and knife-edge slit prompt gamma cameras.

Brent HuismanEnrique MuñozDenis DauvergneJean Michel LétangDavid SarrutÉtienne Testa
Published in: Physics in medicine and biology (2023)
Present and validate an analytical model to calculate efficiency and spatial resolution of multi-parallel slit (MPS) and knife-edge slit (KES) cameras in the context of prompt gamma (PG) imaging in proton therapy, as well as perform a fair comparison between two prototypes of these cameras with their design specifications.

Materials and Methods: Monte Carlo simulations with perfect (ideal) conditions were performed to validate the proposed analytical model, as well as simulations in realistic conditions for the comparison of both prototypes. The spatial resolution obtained from simulations was derived from reconstructed PG profiles. The falloff retrieval precision (FRP) was quantified based on the variability of PG profiles from 50 different realizations.

Results: The analytical model shows that KES and MPS designs fulfilling "MPS-KES similar conditions" should have very close actual performances if the KES slit width corresponds to the half of the MPS slit width. Reconstructed PG profiles from simulated data with both cameras were used to compute the efficiency and spatial resolutions to compare against the model predictions. The FRP of both cameras was calculated with realistic detection conditions for beams with 107 , 108 and 109 incident protons. A good agreement was found between the values predicted by the analytical model and those obtained from Monte Carlo simulations (relative deviations of the
order of 5%).

Conclusion: The MPS camera outperforms the KES camera with their design specifications in realistic conditions and both systems can reach millimetric precision in the determination of the falloff position with 108 or more initial protons.
Keyphrases
  • monte carlo
  • molecular dynamics
  • cardiovascular disease
  • high speed
  • high resolution
  • machine learning
  • electronic health record
  • convolutional neural network
  • fluorescence imaging
  • photodynamic therapy